cleanup and testing

2 files changed, 11 insertions, 26 deletions

diff --git a/mnist/Neuronet.hs b/mnist/Neuronet.hs
index ece288a..6c3ea32 100644
--- a/mnist/Neuronet.hs
+++ b/mnist/Neuronet.hs
@@ -1,18 +1,3 @@
-{- | 
-
-     N E U R O T I C U S
-        
-     A small and straightforward neural network coded in Haskell
-     from scratch. It uses gradient descent and the beauty of
-     backpropagation for learning.
-     
-     TODOS: improve performance, parallelize, CUDA, FFI
-
-     Michal Idziorek <m.i@gmx.at> 
-     March 2019
-
--}
-
 module Neuronet
     ( Neuronet      -- the neuronet
      ,neuronet      -- initalize neuronet
@@ -20,15 +5,16 @@ module Neuronet
      ,trainBatch    -- train with batch
      ,asknet        -- ask the neuroal net
 
-     ,wghtact
+     ,wghtact       
      ,backprop
     )where
 
-import Numeric.LinearAlgebra (Matrix,Vector,tr,scale,cmap,(#>),randn,toList,fromList,toLists,fromLists,outer)
 import Data.List
+import Numeric.LinearAlgebra (Matrix,Vector,tr,scale,cmap,(#>),randn,
+                              toList,fromList,toLists,fromLists,outer)
 
--- | A layer of our network consists of a weight matrix with input weights 
--- and a vector holding the bias at each neuron.
+-- | A layer of our network consists of a weight matrix with input 
+-- weights and a vector holding the bias at each neuron.
 type Layer    = (Matrix Double,Vector Double)
 
 -- | Our neural network is simply a list of layers
@@ -39,15 +25,15 @@ type Neuronet = [Layer]
 -- using gaussian distribution with mean 0 and standard deviation 1.
 neuronet :: [Int] -> IO Neuronet
 neuronet l = mapM nl $ zip l (tail l)
-    where nl (i,l) = (,) <$> randn l i <*> (randn 1 l >>= return.fromList.head.toLists)
+    where nl (i,l) = (,) <$> randn l i <*> 
+                            (randn 1 l >>= return.fromList.head.toLists)
 
 -- | Given the input vector calculate the `weighted inputs` and 
 -- `activations` for all layers of our network.
 wghtact :: Neuronet -> Vector Double -> [(Vector Double,Vector Double)]
 wghtact [] _ = []
-wghtact ((w,b):lx) x = (z,a):wghtact lx a
-    where z = w #> x + b
-          a = cmap sigmoid z
+wghtact ((w,b):lx) x = (z,a):wghtact lx a where z = w #> x + b
+                                                a = cmap sigmoid z
 
 -- | Given the input vector calculate the final output
 asknet :: Neuronet -> Vector Double -> Vector Double
@@ -90,4 +76,3 @@ trainBatch r net xs ys = zipWith (upd r) net bp
 -- | Update a single Layer given the `direction` and `training rate` 
 upd :: Double -> Layer -> (Matrix Double,Vector Double) -> Layer
 upd r (a,b) (c,d) = (a-scale r c,b-scale r d)
-
diff --git a/mnist/main.hs b/mnist/main.hs
index 2810e34..02ba6f7 100644
--- a/mnist/main.hs
+++ b/mnist/main.hs
@@ -59,9 +59,9 @@ read_samples f1 f2 = do
 mainMNIST :: IO ()
 mainMNIST =do
 
-        let ep  = 20        -- number of epochs
+        let ep  = 10        -- number of epochs
         let mbs = 10        -- mini-batch size
-        let lr  = 2         -- learning rate
+        let lr  = 3         -- learning rate
         let cap = 999999    -- cap number of training samples
 
         putStrLn "= Init ="